forked from phoenix/litellm-mirror
2e5c46ef6d
* fix(litellm_logging.py): ensure cache hits are scrubbed if 'turn_off_message_logging' is enabled * fix(sagemaker.py): fix streaming to raise error immediately Fixes https://github.com/BerriAI/litellm/issues/6054 * (fixes) gcs bucket key based logging (#6044) * fixes for gcs bucket logging * fix StandardCallbackDynamicParams * fix - gcs logging when payload is not serializable * add test_add_callback_via_key_litellm_pre_call_utils_gcs_bucket * working success callbacks * linting fixes * fix linting error * add type hints to functions * fixes for dynamic success and failure logging * fix for test_async_chat_openai_stream * fix handle case when key based logging vars are set as os.environ/ vars * fix prometheus track cooldown events on custom logger (#6060) * (docs) add 1k rps load test doc (#6059) * docs 1k rps load test * docs load testing * docs load testing litellm * docs load testing * clean up load test doc * docs prom metrics for load testing * docs using prometheus on load testing * doc load testing with prometheus * (fixes) docs + qa - gcs key based logging (#6061) * fixes for required values for gcs bucket * docs gcs bucket logging * bump: version 1.48.12 → 1.48.13 * ci/cd run again * bump: version 1.48.13 → 1.48.14 * update load test doc * (docs) router settings - on litellm config (#6037) * add yaml with all router settings * add docs for router settings * docs router settings litellm settings * (feat) OpenAI prompt caching models to model cost map (#6063) * add prompt caching for latest models * add cache_read_input_token_cost for prompt caching models * fix(litellm_logging.py): check if param is iterable Fixes https://github.com/BerriAI/litellm/issues/6025#issuecomment-2393929946 * fix(factory.py): support passing an 'assistant_continue_message' to prevent bedrock error Fixes https://github.com/BerriAI/litellm/issues/6053 * fix(databricks/chat): handle streaming responses * fix(factory.py): fix linting error * fix(utils.py): unify anthropic + deepseek prompt caching information to openai format Fixes https://github.com/BerriAI/litellm/issues/6069 * test: fix test * fix(types/utils.py): support all openai roles Fixes https://github.com/BerriAI/litellm/issues/6052 * test: fix test --------- Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
720 lines
24 KiB
Python
720 lines
24 KiB
Python
# What is this?
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## Handler file for databricks API https://docs.databricks.com/en/machine-learning/foundation-models/api-reference.html#chat-request
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import copy
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import json
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import os
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import time
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import types
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from enum import Enum
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from functools import partial
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from typing import Any, Callable, List, Literal, Optional, Tuple, Union
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import httpx # type: ignore
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import requests # type: ignore
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import litellm
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from litellm.litellm_core_utils.core_helpers import map_finish_reason
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from litellm.llms.custom_httpx.http_handler import (
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AsyncHTTPHandler,
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HTTPHandler,
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get_async_httpx_client,
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)
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from litellm.llms.databricks.exceptions import DatabricksError
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from litellm.llms.databricks.streaming_utils import ModelResponseIterator
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from litellm.types.llms.openai import (
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ChatCompletionDeltaChunk,
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ChatCompletionResponseMessage,
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ChatCompletionToolCallChunk,
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ChatCompletionToolCallFunctionChunk,
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ChatCompletionUsageBlock,
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)
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from litellm.types.utils import (
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CustomStreamingDecoder,
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GenericStreamingChunk,
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ProviderField,
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)
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from litellm.utils import CustomStreamWrapper, EmbeddingResponse, ModelResponse, Usage
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from ..base import BaseLLM
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from ..prompt_templates.factory import custom_prompt, prompt_factory
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class DatabricksConfig:
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"""
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Reference: https://docs.databricks.com/en/machine-learning/foundation-models/api-reference.html#chat-request
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"""
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max_tokens: Optional[int] = None
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temperature: Optional[int] = None
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top_p: Optional[int] = None
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top_k: Optional[int] = None
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stop: Optional[Union[List[str], str]] = None
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n: Optional[int] = None
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def __init__(
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self,
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max_tokens: Optional[int] = None,
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temperature: Optional[int] = None,
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top_p: Optional[int] = None,
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top_k: Optional[int] = None,
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stop: Optional[Union[List[str], str]] = None,
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n: Optional[int] = None,
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) -> None:
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locals_ = locals()
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for key, value in locals_.items():
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if key != "self" and value is not None:
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setattr(self.__class__, key, value)
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@classmethod
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def get_config(cls):
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return {
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k: v
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for k, v in cls.__dict__.items()
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if not k.startswith("__")
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and not isinstance(
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v,
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(
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types.FunctionType,
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types.BuiltinFunctionType,
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classmethod,
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staticmethod,
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),
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)
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and v is not None
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}
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def get_required_params(self) -> List[ProviderField]:
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"""For a given provider, return it's required fields with a description"""
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return [
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ProviderField(
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field_name="api_key",
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field_type="string",
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field_description="Your Databricks API Key.",
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field_value="dapi...",
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),
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ProviderField(
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field_name="api_base",
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field_type="string",
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field_description="Your Databricks API Base.",
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field_value="https://adb-..",
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),
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]
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def get_supported_openai_params(self):
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return [
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"stream",
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"stop",
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"temperature",
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"top_p",
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"max_tokens",
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"max_completion_tokens",
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"n",
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]
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def map_openai_params(self, non_default_params: dict, optional_params: dict):
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for param, value in non_default_params.items():
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if param == "max_tokens" or param == "max_completion_tokens":
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optional_params["max_tokens"] = value
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if param == "n":
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optional_params["n"] = value
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if param == "stream" and value is True:
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optional_params["stream"] = value
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if param == "temperature":
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optional_params["temperature"] = value
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if param == "top_p":
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optional_params["top_p"] = value
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if param == "stop":
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optional_params["stop"] = value
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return optional_params
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class DatabricksEmbeddingConfig:
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"""
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Reference: https://learn.microsoft.com/en-us/azure/databricks/machine-learning/foundation-models/api-reference#--embedding-task
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"""
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instruction: Optional[str] = (
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None # An optional instruction to pass to the embedding model. BGE Authors recommend 'Represent this sentence for searching relevant passages:' for retrieval queries
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)
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def __init__(self, instruction: Optional[str] = None) -> None:
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locals_ = locals()
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for key, value in locals_.items():
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if key != "self" and value is not None:
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setattr(self.__class__, key, value)
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@classmethod
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def get_config(cls):
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return {
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k: v
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for k, v in cls.__dict__.items()
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if not k.startswith("__")
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and not isinstance(
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v,
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(
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types.FunctionType,
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types.BuiltinFunctionType,
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classmethod,
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staticmethod,
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),
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)
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and v is not None
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}
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def get_supported_openai_params(
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self,
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): # no optional openai embedding params supported
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return []
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def map_openai_params(self, non_default_params: dict, optional_params: dict):
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return optional_params
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async def make_call(
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client: Optional[AsyncHTTPHandler],
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api_base: str,
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headers: dict,
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data: str,
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model: str,
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messages: list,
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logging_obj,
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streaming_decoder: Optional[CustomStreamingDecoder] = None,
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):
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if client is None:
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client = get_async_httpx_client(
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llm_provider=litellm.LlmProviders.DATABRICKS
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) # Create a new client if none provided
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response = await client.post(api_base, headers=headers, data=data, stream=True)
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if response.status_code != 200:
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raise DatabricksError(status_code=response.status_code, message=response.text)
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if streaming_decoder is not None:
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completion_stream: Any = streaming_decoder.aiter_bytes(
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response.aiter_bytes(chunk_size=1024)
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)
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else:
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completion_stream = ModelResponseIterator(
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streaming_response=response.aiter_lines(), sync_stream=False
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)
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# LOGGING
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logging_obj.post_call(
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input=messages,
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api_key="",
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original_response=completion_stream, # Pass the completion stream for logging
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additional_args={"complete_input_dict": data},
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)
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return completion_stream
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def make_sync_call(
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client: Optional[HTTPHandler],
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api_base: str,
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headers: dict,
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data: str,
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model: str,
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messages: list,
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logging_obj,
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streaming_decoder: Optional[CustomStreamingDecoder] = None,
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):
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if client is None:
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client = litellm.module_level_client # Create a new client if none provided
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response = client.post(api_base, headers=headers, data=data, stream=True)
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if response.status_code != 200:
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raise DatabricksError(status_code=response.status_code, message=response.read())
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if streaming_decoder is not None:
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completion_stream = streaming_decoder.iter_bytes(
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response.iter_bytes(chunk_size=1024)
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)
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else:
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completion_stream = ModelResponseIterator(
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streaming_response=response.iter_lines(), sync_stream=True
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)
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# LOGGING
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logging_obj.post_call(
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input=messages,
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api_key="",
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original_response="first stream response received",
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additional_args={"complete_input_dict": data},
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)
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return completion_stream
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class DatabricksChatCompletion(BaseLLM):
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def __init__(self) -> None:
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super().__init__()
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# makes headers for API call
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def _get_databricks_credentials(
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self, api_key: Optional[str], api_base: Optional[str], headers: Optional[dict]
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) -> Tuple[str, dict]:
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headers = headers or {"Content-Type": "application/json"}
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try:
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from databricks.sdk import WorkspaceClient
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databricks_client = WorkspaceClient()
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api_base = api_base or f"{databricks_client.config.host}/serving-endpoints"
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if api_key is None:
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databricks_auth_headers: dict[str, str] = (
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databricks_client.config.authenticate()
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)
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headers = {**databricks_auth_headers, **headers}
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return api_base, headers
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except ImportError:
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raise DatabricksError(
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status_code=400,
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message=(
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"If the Databricks base URL and API key are not set, the databricks-sdk "
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"Python library must be installed. Please install the databricks-sdk, set "
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"{LLM_PROVIDER}_API_BASE and {LLM_PROVIDER}_API_KEY environment variables, "
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"or provide the base URL and API key as arguments."
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),
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)
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def _validate_environment(
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self,
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api_key: Optional[str],
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api_base: Optional[str],
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endpoint_type: Literal["chat_completions", "embeddings"],
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custom_endpoint: Optional[bool],
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headers: Optional[dict],
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) -> Tuple[str, dict]:
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if api_key is None and headers is None:
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if custom_endpoint:
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raise DatabricksError(
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status_code=400,
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message="Missing API Key - A call is being made to LLM Provider but no key is set either in the environment variables ({LLM_PROVIDER}_API_KEY) or via params",
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)
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else:
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api_base, headers = self._get_databricks_credentials(
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api_base=api_base, api_key=api_key, headers=headers
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)
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if api_base is None:
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if custom_endpoint:
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raise DatabricksError(
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status_code=400,
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message="Missing API Base - A call is being made to LLM Provider but no api base is set either in the environment variables ({LLM_PROVIDER}_API_KEY) or via params",
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)
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else:
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api_base, headers = self._get_databricks_credentials(
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api_base=api_base, api_key=api_key, headers=headers
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)
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if headers is None:
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headers = {
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"Authorization": "Bearer {}".format(api_key),
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"Content-Type": "application/json",
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}
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else:
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if api_key is not None:
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headers.update({"Authorization": "Bearer {}".format(api_key)})
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if api_key is not None:
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headers["Authorization"] = f"Bearer {api_key}"
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if endpoint_type == "chat_completions" and custom_endpoint is not True:
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api_base = "{}/chat/completions".format(api_base)
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elif endpoint_type == "embeddings" and custom_endpoint is not True:
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api_base = "{}/embeddings".format(api_base)
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return api_base, headers
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async def acompletion_stream_function(
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self,
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model: str,
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messages: list,
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custom_llm_provider: str,
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api_base: str,
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custom_prompt_dict: dict,
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model_response: ModelResponse,
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print_verbose: Callable,
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encoding,
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api_key,
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logging_obj,
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stream,
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data: dict,
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optional_params=None,
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litellm_params=None,
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logger_fn=None,
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headers={},
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client: Optional[AsyncHTTPHandler] = None,
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streaming_decoder: Optional[CustomStreamingDecoder] = None,
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) -> CustomStreamWrapper:
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data["stream"] = True
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completion_stream = await make_call(
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client=client,
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api_base=api_base,
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headers=headers,
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data=json.dumps(data),
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model=model,
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messages=messages,
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logging_obj=logging_obj,
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streaming_decoder=streaming_decoder,
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)
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streamwrapper = CustomStreamWrapper(
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completion_stream=completion_stream,
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model=model,
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custom_llm_provider=custom_llm_provider,
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logging_obj=logging_obj,
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)
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return streamwrapper
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async def acompletion_function(
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self,
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model: str,
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messages: list,
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api_base: str,
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custom_prompt_dict: dict,
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model_response: ModelResponse,
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custom_llm_provider: str,
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print_verbose: Callable,
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encoding,
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api_key,
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logging_obj,
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stream,
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data: dict,
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base_model: Optional[str],
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optional_params: dict,
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litellm_params=None,
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logger_fn=None,
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headers={},
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timeout: Optional[Union[float, httpx.Timeout]] = None,
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) -> ModelResponse:
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if timeout is None:
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timeout = httpx.Timeout(timeout=600.0, connect=5.0)
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self.async_handler = AsyncHTTPHandler(timeout=timeout)
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try:
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response = await self.async_handler.post(
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api_base, headers=headers, data=json.dumps(data)
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)
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response.raise_for_status()
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response_json = response.json()
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except httpx.HTTPStatusError as e:
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raise DatabricksError(
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status_code=e.response.status_code,
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message=e.response.text,
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)
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except httpx.TimeoutException:
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raise DatabricksError(status_code=408, message="Timeout error occurred.")
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except Exception as e:
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raise DatabricksError(status_code=500, message=str(e))
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logging_obj.post_call(
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input=messages,
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api_key="",
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original_response=response_json,
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additional_args={"complete_input_dict": data},
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)
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response = ModelResponse(**response_json)
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response.model = custom_llm_provider + "/" + (response.model or "")
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if base_model is not None:
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response._hidden_params["model"] = base_model
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return response
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def completion(
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self,
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model: str,
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messages: list,
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api_base: str,
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custom_llm_provider: str,
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custom_prompt_dict: dict,
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model_response: ModelResponse,
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print_verbose: Callable,
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encoding,
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api_key: Optional[str],
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logging_obj,
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optional_params: dict,
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acompletion=None,
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litellm_params=None,
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logger_fn=None,
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headers: Optional[dict] = None,
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timeout: Optional[Union[float, httpx.Timeout]] = None,
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client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
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custom_endpoint: Optional[bool] = None,
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streaming_decoder: Optional[
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CustomStreamingDecoder
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] = None, # if openai-compatible api needs custom stream decoder - e.g. sagemaker
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):
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custom_endpoint = custom_endpoint or optional_params.pop(
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"custom_endpoint", None
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)
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base_model: Optional[str] = optional_params.pop("base_model", None)
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api_base, headers = self._validate_environment(
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api_base=api_base,
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api_key=api_key,
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endpoint_type="chat_completions",
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custom_endpoint=custom_endpoint,
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headers=headers,
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)
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## Load Config
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config = litellm.DatabricksConfig().get_config()
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for k, v in config.items():
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if (
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k not in optional_params
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): # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in
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optional_params[k] = v
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stream: bool = optional_params.get("stream", None) or False
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optional_params["stream"] = stream
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data = {
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"model": model,
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"messages": messages,
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**optional_params,
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}
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## LOGGING
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logging_obj.pre_call(
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input=messages,
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api_key=api_key,
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additional_args={
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"complete_input_dict": data,
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"api_base": api_base,
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"headers": headers,
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},
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)
|
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if acompletion is True:
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if client is not None and isinstance(client, HTTPHandler):
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client = None
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if (
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stream is not None and stream is True
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): # if function call - fake the streaming (need complete blocks for output parsing in openai format)
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print_verbose("makes async anthropic streaming POST request")
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data["stream"] = stream
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return self.acompletion_stream_function(
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model=model,
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messages=messages,
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data=data,
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api_base=api_base,
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custom_prompt_dict=custom_prompt_dict,
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model_response=model_response,
|
|
print_verbose=print_verbose,
|
|
encoding=encoding,
|
|
api_key=api_key,
|
|
logging_obj=logging_obj,
|
|
optional_params=optional_params,
|
|
stream=stream,
|
|
litellm_params=litellm_params,
|
|
logger_fn=logger_fn,
|
|
headers=headers,
|
|
client=client,
|
|
custom_llm_provider=custom_llm_provider,
|
|
streaming_decoder=streaming_decoder,
|
|
)
|
|
else:
|
|
return self.acompletion_function(
|
|
model=model,
|
|
messages=messages,
|
|
data=data,
|
|
api_base=api_base,
|
|
custom_prompt_dict=custom_prompt_dict,
|
|
custom_llm_provider=custom_llm_provider,
|
|
model_response=model_response,
|
|
print_verbose=print_verbose,
|
|
encoding=encoding,
|
|
api_key=api_key,
|
|
logging_obj=logging_obj,
|
|
optional_params=optional_params,
|
|
stream=stream,
|
|
litellm_params=litellm_params,
|
|
logger_fn=logger_fn,
|
|
headers=headers,
|
|
timeout=timeout,
|
|
base_model=base_model,
|
|
)
|
|
else:
|
|
## COMPLETION CALL
|
|
if stream is True:
|
|
completion_stream = make_sync_call(
|
|
client=(
|
|
client
|
|
if client is not None and isinstance(client, HTTPHandler)
|
|
else None
|
|
),
|
|
api_base=api_base,
|
|
headers=headers,
|
|
data=json.dumps(data),
|
|
model=model,
|
|
messages=messages,
|
|
logging_obj=logging_obj,
|
|
streaming_decoder=streaming_decoder,
|
|
)
|
|
# completion_stream.__iter__()
|
|
return CustomStreamWrapper(
|
|
completion_stream=completion_stream,
|
|
model=model,
|
|
custom_llm_provider=custom_llm_provider,
|
|
logging_obj=logging_obj,
|
|
)
|
|
else:
|
|
if client is None or not isinstance(client, HTTPHandler):
|
|
client = HTTPHandler(timeout=timeout) # type: ignore
|
|
try:
|
|
response = client.post(
|
|
api_base, headers=headers, data=json.dumps(data)
|
|
)
|
|
response.raise_for_status()
|
|
|
|
response_json = response.json()
|
|
except httpx.HTTPStatusError as e:
|
|
raise DatabricksError(
|
|
status_code=e.response.status_code,
|
|
message=e.response.text,
|
|
)
|
|
except httpx.TimeoutException:
|
|
raise DatabricksError(
|
|
status_code=408, message="Timeout error occurred."
|
|
)
|
|
except Exception as e:
|
|
raise DatabricksError(status_code=500, message=str(e))
|
|
|
|
response = ModelResponse(**response_json)
|
|
|
|
response.model = custom_llm_provider + "/" + (response.model or "")
|
|
|
|
if base_model is not None:
|
|
response._hidden_params["model"] = base_model
|
|
|
|
return response
|
|
|
|
async def aembedding(
|
|
self,
|
|
input: list,
|
|
data: dict,
|
|
model_response: ModelResponse,
|
|
timeout: float,
|
|
api_key: str,
|
|
api_base: str,
|
|
logging_obj,
|
|
headers: dict,
|
|
client=None,
|
|
) -> EmbeddingResponse:
|
|
response = None
|
|
try:
|
|
if client is None or isinstance(client, AsyncHTTPHandler):
|
|
self.async_client = AsyncHTTPHandler(timeout=timeout) # type: ignore
|
|
else:
|
|
self.async_client = client
|
|
|
|
try:
|
|
response = await self.async_client.post(
|
|
api_base,
|
|
headers=headers,
|
|
data=json.dumps(data),
|
|
) # type: ignore
|
|
|
|
response.raise_for_status()
|
|
|
|
response_json = response.json()
|
|
except httpx.HTTPStatusError as e:
|
|
raise DatabricksError(
|
|
status_code=e.response.status_code,
|
|
message=response.text if response else str(e),
|
|
)
|
|
except httpx.TimeoutException:
|
|
raise DatabricksError(
|
|
status_code=408, message="Timeout error occurred."
|
|
)
|
|
except Exception as e:
|
|
raise DatabricksError(status_code=500, message=str(e))
|
|
|
|
## LOGGING
|
|
logging_obj.post_call(
|
|
input=input,
|
|
api_key=api_key,
|
|
additional_args={"complete_input_dict": data},
|
|
original_response=response_json,
|
|
)
|
|
return EmbeddingResponse(**response_json)
|
|
except Exception as e:
|
|
## LOGGING
|
|
logging_obj.post_call(
|
|
input=input,
|
|
api_key=api_key,
|
|
original_response=str(e),
|
|
)
|
|
raise e
|
|
|
|
def embedding(
|
|
self,
|
|
model: str,
|
|
input: list,
|
|
timeout: float,
|
|
logging_obj,
|
|
api_key: Optional[str],
|
|
api_base: Optional[str],
|
|
optional_params: dict,
|
|
model_response: Optional[litellm.utils.EmbeddingResponse] = None,
|
|
client=None,
|
|
aembedding=None,
|
|
headers: Optional[dict] = None,
|
|
) -> EmbeddingResponse:
|
|
api_base, headers = self._validate_environment(
|
|
api_base=api_base,
|
|
api_key=api_key,
|
|
endpoint_type="embeddings",
|
|
custom_endpoint=False,
|
|
headers=headers,
|
|
)
|
|
model = model
|
|
data = {"model": model, "input": input, **optional_params}
|
|
|
|
## LOGGING
|
|
logging_obj.pre_call(
|
|
input=input,
|
|
api_key=api_key,
|
|
additional_args={"complete_input_dict": data, "api_base": api_base},
|
|
)
|
|
|
|
if aembedding is True:
|
|
return self.aembedding(data=data, input=input, logging_obj=logging_obj, model_response=model_response, api_base=api_base, api_key=api_key, timeout=timeout, client=client, headers=headers) # type: ignore
|
|
if client is None or isinstance(client, AsyncHTTPHandler):
|
|
self.client = HTTPHandler(timeout=timeout) # type: ignore
|
|
else:
|
|
self.client = client
|
|
|
|
## EMBEDDING CALL
|
|
try:
|
|
response = self.client.post(
|
|
api_base,
|
|
headers=headers,
|
|
data=json.dumps(data),
|
|
) # type: ignore
|
|
|
|
response.raise_for_status() # type: ignore
|
|
|
|
response_json = response.json() # type: ignore
|
|
except httpx.HTTPStatusError as e:
|
|
raise DatabricksError(
|
|
status_code=e.response.status_code,
|
|
message=e.response.text,
|
|
)
|
|
except httpx.TimeoutException:
|
|
raise DatabricksError(status_code=408, message="Timeout error occurred.")
|
|
except Exception as e:
|
|
raise DatabricksError(status_code=500, message=str(e))
|
|
|
|
## LOGGING
|
|
logging_obj.post_call(
|
|
input=input,
|
|
api_key=api_key,
|
|
additional_args={"complete_input_dict": data},
|
|
original_response=response_json,
|
|
)
|
|
|
|
return litellm.EmbeddingResponse(**response_json)
|